Evaluation of Emergency Social Media Language Efficiency Based on Persuasion Theory and Data Envelopment Analysis: A Case Study of the 2025 Beijing Extreme Rainfall Event

In the context of urban extreme weather events, the efficacy of the “emergency language” employed by governments and public institutions on social media in effectively reaching and guiding the public in a timely manner necessitates a quantifiable evaluation framework. An indicator system was constructed on the basis of Hovland’s persuasion theory. This system comprised five input characteristics (word count/structural clarity, first/second-person perspective, emotional appeal, evidence and framing, and media format) along with three output indicators (reposts, comments, and likes). A data envelopment analysis (DEA) model that is oriented towards output was employed, with disseminators being categorized into four distinct decision-making units: central mainstream media, other government media, local government media, and other media. It is imperative to note that the outputs were subjected to a process of normalization through the implementation of a scale factor. The data were sourced from the Weibo platform within the specified time window, which was from 10:00 on 24 July 2025, to 12:00 on 19 August 2025, with a sample size of 744. The findings revealed substantial disparities in technical efficiency across different disseminator types. A subset of local government media demonstrated a technical efficiency ≈ 1.00 yet low scale efficiency. Posts exhibiting clear structures, actionable points, and accompanying images or videos achieved higher cross-efficiency scores. It is therefore evident that the proposed DEA model provides a benchmark for maximizing dissemination effectiveness under given information characteristics. It is recommended that posting frequencies be maintained at consistent intervals during periods of heightened activity, that a template structure be adopted in accordance with the “fact–action–assistance channel” model, and that the proportion of rich media content be augmented.